7,648 research outputs found
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201
RGB-D And Thermal Sensor Fusion: A Systematic Literature Review
In the last decade, the computer vision field has seen significant progress
in multimodal data fusion and learning, where multiple sensors, including
depth, infrared, and visual, are used to capture the environment across diverse
spectral ranges. Despite these advancements, there has been no systematic and
comprehensive evaluation of fusing RGB-D and thermal modalities to date. While
autonomous driving using LiDAR, radar, RGB, and other sensors has garnered
substantial research interest, along with the fusion of RGB and depth
modalities, the integration of thermal cameras and, specifically, the fusion of
RGB-D and thermal data, has received comparatively less attention. This might
be partly due to the limited number of publicly available datasets for such
applications. This paper provides a comprehensive review of both,
state-of-the-art and traditional methods used in fusing RGB-D and thermal
camera data for various applications, such as site inspection, human tracking,
fault detection, and others. The reviewed literature has been categorised into
technical areas, such as 3D reconstruction, segmentation, object detection,
available datasets, and other related topics. Following a brief introduction
and an overview of the methodology, the study delves into calibration and
registration techniques, then examines thermal visualisation and 3D
reconstruction, before discussing the application of classic feature-based
techniques as well as modern deep learning approaches. The paper concludes with
a discourse on current limitations and potential future research directions. It
is hoped that this survey will serve as a valuable reference for researchers
looking to familiarise themselves with the latest advancements and contribute
to the RGB-DT research field.Comment: 33 pages, 20 figure
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.Comment: ECCV 201
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Introduction to multimodal scene understanding
A fundamental goal of computer vision is to discover the semantic information within a given scene, commonly referred to as scene understanding. The overall goal is to find a mapping to derive semantic information from sensor data, which is an extremely challenging task, partially due to the ambiguities in the appearance of the data. However, the majority of the scene understanding tasks tackled so far are mainly involving visual modalities only. In this book, we aim at providing an overview of recent advances in algorithms and applications that involve multiple sources of information for scene understanding. In this context, deep learning models are particularly suitable for combining multiple modalities and, as a matter of fact, many contributions are dealing with such architectures to take benefit of all data streams and obtain optimal performances. We conclude this book’s introduction by a concise description of the rest of the chapters therein contained. They are focused at providing an understanding of the state-of-the-art, open problems, and future directions related to multimodal scene understanding as a scientific discipline.</p
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